Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

作者:Zhengyou Zhang, Zicheng Liu, Dennis Adler, Michael F. Cohen, Erik Hanson, Ying Shan

摘要

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.

论文关键词:face modeling, facial animation, geometric modeling, structure from motion, model-based bundle adjustment, head tracking

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论文官网地址:https://doi.org/10.1023/B:VISI.0000015915.50080.85